Instructions to use vasugoel/K-12BERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vasugoel/K-12BERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="vasugoel/K-12BERT")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("vasugoel/K-12BERT") model = AutoModelForMaskedLM.from_pretrained("vasugoel/K-12BERT") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 685b58d56e632c47b8aef45dc480f7ca5222a6426a7a4623dec84e6d19b56d0a
- Size of remote file:
- 438 MB
- SHA256:
- 208d12aa46dcc0a6b6e46f0532da4221c145f6c0c90171d8e9fbea7775de4947
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